Development of a biomimetic liver tumor-on-a-chip model based on decellularized liver matrix for toxicity testing
Bibliographic record
Abstract
Cancer poses a great health threat to both developed and developing countries, and anti-cancer drugs are of important interest for improved clinical outcomes. Although tumor-on-a-chip technologies offer a feasible approach to screening drug toxicity, their capability to mimic the native tumor microenvironment (TME) is still limited. For better mimicry of the TME, we developed a biomimetic three-dimensional (3D) liver tumor-on-a-chip with the integration of essential components derived from decellularized liver matrix (DLM) with gelatin methacryloyl (GelMA) in a microfluidics-based 3D dynamic cell culture system. The biomimetic liver tumor-on-a-chip based on the integration of DLM components with GelMA, as opposed to GelMA only, had an increased capability to maintain cell viability and to enhance hepatocyte functions under flow conditions. The improved performance of the DLM-GelMA-based tumor-on-a-chip may be attributed to the provision of biochemical factors (e.g., growth factors), the preservation of scaffold proteins, and the reestablishment of biophysical cues (e.g., stiffness and shear stress) for better recapitulation of the 3D liver TME. Furthermore, this DLM-GelMA-based tumor-on-a-chip exhibited linear dose-dependent drug responses to the toxicity of acetaminophen and sorafenib. Taken together, our study demonstrates that the DLM-GelMA-based biomimetic liver tumor-on-a-chip better mimics the in vivo TME and holds great promise for a breadth of pathological and pharmacological studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".